I just competed in Google's AI Agent Bake-Off—a high-pressure, 5-hour challenge where teams had to build a fully functional agent-powered banking application completely from scratch. Thanks to the right strategies (and a lot of caffeine), I was able to win the challenge. You can watch the full AI Agent Bake-Off competition here: 👉 https://youtu.be/0CQxF56MKWo?si=7cpRbwkucBuXcwCz But if you want to see what really happened behind the scenes, I'm breaking down the exact 5 strategies I used so you can apply them to your own AI agent projects. Here's what you'll learn: ✅ How to use AI reference projects to jumpstart your development and build faster ✅ The power of "talking to your computer" to prototype and debug with natural language ✅ Why creating an agent workflow digital twin saves you hours of coding time ✅ How to train AI to work directly with your tech stack and APIs ✅ The secret to parallel AI development for rapid testing and iteration Plus, I'm giving away the complete source code from the winning banking app—so you can see real, working examples of these strategies in action! 👉 https://github.com/bhancockio/ai_agent_bake_off_ep_2 Whether you're building agents for the first time or looking to level up your workflow, these 5 tips will help you work smarter and faster. Cheers, Brandon Hancock 🧑‍💻 P.S. Ready to build real-world AI applications in days instead of months? Check out shipkit.ai for pre-built, production-ready templates covering the most common AI use cases: AI Chat, RAG systems, & ADK agents. Skip the boilerplate and start shipping faster. Use code BAKEOFF for $50 off during checkout. 👉 shipkit.ai

How relevant Google ADK compared to OpenAI Agent Builder? OpenAI looks super simple. Should we migrate?

Past 6 months I’ve worked with 4 different teams rolling out Ai agents. And you know the deciding factor wasn’t the model, the framework, or even the prompts, it was grounding. Ai agents sound brilliant when you demo them in isolation. but in the real world, even the smart-sounding ones fail miserably. that's because Customers don’t want creativity, they want consistency. And that’s where grounding makes or breaks an agent. What I found was simple, feedback loops only worked when we stepped in manually, reflection slowed things down, code agents broke once tasks got messy, RLAIF collapsed outside demos, skill acquisition was hype, drift was unavoidable, and QA, unglamorous but relentless, was the only real driver of reliability.

The market just keeps getting more competitive, which is great news for us. https://help.openai.com/en/articles/11369540-using-codex-with-your-chatgpt-plan

Hey guys, I’m building an AI agent / SaaS . This is an actual product that I want to deploy, monetize not just a toy project. I’m using ADK for the agent creation. The issue: - The first message I send to the agent takes about 20 seconds to get a response. - Every subsequent message takes another 10 seconds. - At this speed, it’s simply not usable, no real user will ever wait that long. Right now I’m stuck. I honestly can’t believe this is ADK’s fault, because if it is, then it’s basically unusable in production. So I assume it’s something I’m doing wrong, but I have no idea what. All the Google Cloud setup/config stuff is so complicated, and I feel lost. For more details, I posted here on the Google ADK GitHub: 👉 https://github.com/google/adk-python/issues/2704